Diss Analyses
Justin Sanchez
2024-05-20
| Total Words | Mean Words | Total Characters | Mean Characters |
|---|---|---|---|
| 37536 | 74.48 | 208786 | 414.26 |
1 Descriptives
1.1 Conditions
| Video.Conditions | Count |
|---|---|
| 0 = Negative condition | 170 |
| 1 = Neutral condition | 171 |
| 2 = Positive condition | 162 |
| Total | 503 |
| Factor | Eigenvalue | Education Loading | Occupation Loading | Income Loading |
|---|---|---|---|---|
| Factor 1 | 1.6498582 | 0.7682769 | 0.7025094 | 0.7523891 |
| Factor 2 | 0.7247275 | 0.7682769 | 0.7025094 | 0.7523891 |
| Factor 3 | 0.6254143 | 0.7682769 | 0.7025094 | 0.7523891 |
1.2 Descriptives Table
| n | mean | sd | median | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|
| Distributive Justice | 503 | 2.12 | 1.30 | 2.20 | 0.00 | 4 | 0 - 4 | -0.19 | -1.23 | 0.06 |
| Police Effectiveness | 503 | 2.26 | 1.13 | 2.33 | 0.00 | 4 | 0 - 4 | -0.41 | -0.76 | 0.05 |
| Legal Cynicism | 503 | 2.70 | 0.85 | 2.80 | 0.00 | 4 | 0 - 4 | -0.66 | 0.08 | 0.04 |
| Expected PJ | 503 | 3.14 | 1.04 | 3.50 | 0.00 | 4 | 0 - 4 | -1.45 | 1.34 | 0.05 |
| Global Procedural Justice | 503 | 2.02 | 1.08 | 2.00 | 0.00 | 4 | 0 - 4 | -0.09 | -0.81 | 0.05 |
| Specific Procedural Justice | 503 | 2.36 | 1.18 | 2.50 | 0.00 | 4 | 0 - 4 | -0.16 | -1.20 | 0.05 |
| Social Desirability Scale | 503 | 0.56 | 0.25 | 0.59 | 0.00 | 1 | 0 - 1 | -0.25 | -0.75 | 0.01 |
| Self Control | 503 | 2.53 | 0.76 | 2.58 | 0.25 | 4 | 0.25 - 4 | -0.29 | -0.36 | 0.03 |
| Normative Legitimacy | 503 | 2.61 | 1.06 | 2.80 | 0.00 | 4 | 0 - 4 | -0.68 | -0.14 | 0.05 |
| Non-norm Legitimacy | 503 | 2.77 | 0.96 | 3.00 | 0.00 | 4 | 0 - 4 | -0.76 | -0.07 | 0.04 |
| Male dichotomized (Male = 0) | 502 | 0.53 | 0.52 | 1.00 | 0.00 | 2 | 0 - 2 | 0.14 | -1.41 | 0.02 |
| Income ($50,000-$74,999 = 2) | 500 | 1.97 | 1.46 | 2.00 | 0.00 | 4 | 0 - 4 | 0.03 | -1.34 | 0.07 |
| Education (Bachelor’s degree = 2) | 502 | 1.71 | 0.77 | 2.00 | 0.00 | 3 | 0 - 3 | 0.50 | -1.07 | 0.03 |
| Occupation (Unemployed = 0) | 502 | 1.96 | 1.29 | 3.00 | 0.00 | 3 | 0 - 3 | -0.63 | -1.39 | 0.06 |
| Political Scale (Centrist = 3) | 350 | 3.00 | 1.67 | 3.00 | 0.00 | 5 | 0 - 5 | -0.13 | -1.21 | 0.09 |
| Citizen (No = 0) | 501 | 0.97 | 0.17 | 1.00 | 0.00 | 1 | 0 - 1 | -5.50 | 28.31 | 0.01 |
| Police Family (No = 0) | 503 | 0.15 | 0.35 | 0.00 | 0.00 | 1 | 0 - 1 | 1.99 | 1.95 | 0.02 |
| Police Contact (No = 0) | 502 | 0.30 | 0.46 | 0.00 | 0.00 | 1 | 0 - 1 | 0.85 | -1.29 | 0.02 |
| Arrested | 503 | 0.19 | 0.39 | 0.00 | 0.00 | 1 | 0 - 1 | 1.57 | 0.46 | 0.02 |
| Age | 500 | 46.04 | 15.99 | 46.00 | 18.00 | 83 | 18 - 83 | 0.07 | -1.09 | 0.72 |
| Race dichotomized (White = 0) | 491 | 0.29 | 0.46 | 0.00 | 0.00 | 1 | 0 - 1 | 0.91 | -1.18 | 0.02 |
| Type of community (Rural = 1) | 501 | 0.18 | 0.38 | 0.00 | 0.00 | 1 | 0 - 1 | 1.66 | 0.77 | 0.02 |
| Region of country (South = 1) | 502 | 0.35 | 0.48 | 0.00 | 0.00 | 1 | 0 - 1 | 0.62 | -1.61 | 0.02 |
1.3 Bar Graphs
2 Cronbach’s Alphas,
Boxplots, and Histograms
2.1 Distributive Justice
##
## Reliability analysis
## Call: alpha(x = df[, c("dist_just1", "dist_just2", "dist_just3", "dist_just4",
## "dist_just5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.97 0.96 0.85 28 0.0025 2.1 1.3 0.85
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.96 0.97 0.97
## Duhachek 0.96 0.97 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## dist_just1 0.95 0.95 0.94 0.84 20 0.0034 0.00036 0.83
## dist_just2 0.95 0.95 0.94 0.83 20 0.0034 0.00023 0.84
## dist_just3 0.96 0.96 0.95 0.85 23 0.0031 0.00112 0.85
## dist_just4 0.96 0.96 0.95 0.85 24 0.0030 0.00113 0.85
## dist_just5 0.96 0.96 0.95 0.86 24 0.0029 0.00084 0.85
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## dist_just1 501 0.95 0.95 0.94 0.92 2.1 1.4
## dist_just2 502 0.95 0.95 0.95 0.93 2.0 1.4
## dist_just3 503 0.93 0.93 0.91 0.89 2.0 1.4
## dist_just4 503 0.93 0.93 0.90 0.89 2.2 1.3
## dist_just5 502 0.92 0.92 0.89 0.88 2.3 1.4
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## dist_just1 0.19 0.21 0.14 0.28 0.18 0.01
## dist_just2 0.21 0.20 0.13 0.30 0.17 0.00
## dist_just3 0.21 0.20 0.13 0.32 0.16 0.00
## dist_just4 0.15 0.20 0.14 0.34 0.17 0.00
## dist_just5 0.14 0.16 0.16 0.31 0.23 0.00
2.2 Police Effectiveness
##
## Reliability analysis
## Call: alpha(x = df[, c("pol_effect1", "pol_effect2", "pol_effect3")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.87 0.77 10 0.0071 2.3 1.1 0.77
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.91 0.92
## Duhachek 0.89 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pol_effect1 0.87 0.87 0.77 0.77 6.5 0.0118 NA 0.77
## pol_effect2 0.85 0.85 0.74 0.74 5.7 0.0133 NA 0.74
## pol_effect3 0.89 0.89 0.80 0.80 8.1 0.0099 NA 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pol_effect1 502 0.92 0.92 0.86 0.82 2.2 1.2
## pol_effect2 503 0.93 0.93 0.88 0.84 2.4 1.2
## pol_effect3 502 0.91 0.91 0.83 0.79 2.2 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## pol_effect1 0.11 0.18 0.23 0.35 0.13 0
## pol_effect2 0.09 0.15 0.20 0.37 0.19 0
## pol_effect3 0.13 0.18 0.22 0.33 0.14 0
2.3 Legal Cynicism
##
## Reliability analysis
## Call: alpha(x = df[, c("leg_cyn1R", "leg_cyn2", "leg_cyn3", "leg_cyn4R",
## "leg_cyn5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.8 0.78 0.44 3.9 0.014 2.7 0.85 0.44
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.82
## Duhachek 0.76 0.79 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## leg_cyn1R 0.72 0.74 0.70 0.41 2.8 0.020 0.0193 0.42
## leg_cyn2 0.73 0.73 0.69 0.40 2.7 0.019 0.0227 0.41
## leg_cyn3 0.80 0.81 0.77 0.51 4.2 0.014 0.0056 0.54
## leg_cyn4R 0.77 0.78 0.74 0.47 3.5 0.016 0.0131 0.49
## leg_cyn5 0.73 0.73 0.70 0.40 2.7 0.019 0.0224 0.43
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## leg_cyn1R 502 0.81 0.79 0.73 0.66 2.7 1.3
## leg_cyn2 502 0.79 0.81 0.75 0.66 2.9 1.1
## leg_cyn3 503 0.58 0.62 0.47 0.40 3.0 1.0
## leg_cyn4R 503 0.73 0.70 0.59 0.52 1.9 1.3
## leg_cyn5 502 0.78 0.80 0.74 0.66 3.0 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## leg_cyn1R 0.08 0.15 0.11 0.34 0.31 0
## leg_cyn2 0.04 0.09 0.09 0.45 0.33 0
## leg_cyn3 0.02 0.08 0.12 0.41 0.37 0
## leg_cyn4R 0.16 0.30 0.16 0.22 0.15 0
## leg_cyn5 0.03 0.05 0.14 0.42 0.36 0
2.4 Expected PJ
##
## Reliability analysis
## Call: alpha(x = df[, c("expected_pj1", "expected_pj2", "expected_pj3",
## "expected_pj4", "expected_pj5", "expected_pj6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.96 0.79 23 0.003 3.1 1 0.8
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.96 0.96
## Duhachek 0.95 0.96 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## expected_pj1 0.94 0.95 0.94 0.78 17 0.0041 0.0029 0.79
## expected_pj2 0.95 0.95 0.94 0.78 18 0.0039 0.0043 0.79
## expected_pj3 0.95 0.95 0.94 0.79 19 0.0038 0.0036 0.80
## expected_pj4 0.96 0.96 0.95 0.82 24 0.0029 0.0011 0.82
## expected_pj5 0.95 0.95 0.94 0.79 19 0.0038 0.0054 0.82
## expected_pj6 0.95 0.95 0.95 0.79 19 0.0037 0.0036 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## expected_pj1 502 0.94 0.94 0.93 0.91 3.2 1.1
## expected_pj2 500 0.92 0.92 0.91 0.89 3.2 1.1
## expected_pj3 498 0.92 0.92 0.90 0.87 3.2 1.2
## expected_pj4 503 0.85 0.85 0.81 0.78 2.9 1.2
## expected_pj5 500 0.91 0.91 0.89 0.87 3.1 1.1
## expected_pj6 502 0.91 0.91 0.89 0.87 3.3 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## expected_pj1 0.04 0.07 0.06 0.26 0.57 0.00
## expected_pj2 0.04 0.07 0.10 0.26 0.53 0.01
## expected_pj3 0.06 0.07 0.07 0.21 0.59 0.01
## expected_pj4 0.06 0.11 0.11 0.32 0.40 0.00
## expected_pj5 0.05 0.07 0.09 0.33 0.45 0.01
## expected_pj6 0.04 0.06 0.06 0.23 0.61 0.00
2.5 Global Procedural Justice
##
## Reliability analysis
## Call: alpha(x = df[, c("global_pj1", "global_pj2", "global_pj3", "global_pj4",
## "global_pj5", "global_pj6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.95 0.78 22 0.0031 2 1.1 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.96 0.96
## Duhachek 0.95 0.96 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## global_pj1 0.94 0.94 0.94 0.77 16 0.0041 0.0020 0.77
## global_pj2 0.95 0.95 0.94 0.78 18 0.0039 0.0031 0.78
## global_pj3 0.95 0.95 0.94 0.78 18 0.0038 0.0022 0.78
## global_pj4 0.95 0.95 0.94 0.79 19 0.0036 0.0030 0.79
## global_pj5 0.95 0.95 0.94 0.80 19 0.0035 0.0022 0.79
## global_pj6 0.95 0.95 0.95 0.79 19 0.0036 0.0022 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## global_pj1 503 0.94 0.94 0.93 0.91 2.1 1.2
## global_pj2 499 0.91 0.91 0.90 0.87 2.1 1.2
## global_pj3 501 0.91 0.91 0.90 0.87 2.0 1.2
## global_pj4 503 0.89 0.89 0.87 0.85 1.9 1.2
## global_pj5 501 0.88 0.88 0.85 0.83 1.9 1.2
## global_pj6 503 0.89 0.89 0.86 0.84 2.2 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## global_pj1 0.12 0.21 0.24 0.31 0.12 0.00
## global_pj2 0.10 0.23 0.25 0.31 0.11 0.01
## global_pj3 0.12 0.25 0.27 0.26 0.10 0.01
## global_pj4 0.15 0.27 0.22 0.28 0.08 0.00
## global_pj5 0.13 0.25 0.23 0.29 0.09 0.01
## global_pj6 0.10 0.22 0.24 0.31 0.13 0.00
2.6 Specific Procedural Justice
##
## Reliability analysis
## Call: alpha(x = df[, c("specific_pj1", "specific_pj2", "specific_pj3",
## "specific_pj4", "specific_pj5", "specific_pj6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.92 0.94 0.64 11 0.0054 2.4 1.2 0.67
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## specific_pj1 0.88 0.88 0.90 0.61 7.7 0.0078 0.019 0.59
## specific_pj2 0.88 0.89 0.90 0.61 8.0 0.0076 0.019 0.64
## specific_pj3 0.91 0.91 0.92 0.66 9.6 0.0057 0.027 0.70
## specific_pj4 0.90 0.90 0.91 0.65 9.1 0.0065 0.019 0.65
## specific_pj5 0.89 0.90 0.91 0.65 9.1 0.0066 0.019 0.65
## specific_pj6 0.92 0.92 0.93 0.69 11.3 0.0054 0.016 0.70
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## specific_pj1 502 0.93 0.91 0.92 0.88 2.4 1.60
## specific_pj2 501 0.91 0.90 0.90 0.86 2.3 1.65
## specific_pj3 503 0.78 0.81 0.76 0.70 3.0 1.13
## specific_pj4 502 0.86 0.83 0.81 0.77 1.6 1.54
## specific_pj5 502 0.85 0.84 0.81 0.78 1.6 1.48
## specific_pj6 503 0.69 0.74 0.67 0.61 3.3 0.91
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## specific_pj1 0.22 0.13 0.09 0.18 0.39 0.00
## specific_pj2 0.26 0.11 0.09 0.18 0.36 0.01
## specific_pj3 0.05 0.06 0.16 0.30 0.43 0.00
## specific_pj4 0.37 0.20 0.12 0.11 0.19 0.00
## specific_pj5 0.34 0.15 0.23 0.11 0.17 0.00
## specific_pj6 0.02 0.04 0.11 0.33 0.51 0.00
2.7 Normative Legitimacy
##
## Reliability analysis
## Call: alpha(x = df[, c("norm_leg1", "norm_leg2", "norm_leg3", "norm_leg4",
## "norm_leg5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.91 0.69 11 0.0058 2.6 1.1 0.7
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## norm_leg1 0.89 0.89 0.87 0.68 8.4 0.0078 0.0014 0.69
## norm_leg2 0.90 0.90 0.87 0.69 8.8 0.0076 0.0026 0.69
## norm_leg3 0.90 0.90 0.88 0.69 9.1 0.0073 0.0047 0.69
## norm_leg4 0.89 0.89 0.87 0.68 8.5 0.0077 0.0039 0.67
## norm_leg5 0.91 0.91 0.89 0.73 10.6 0.0063 0.0012 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## norm_leg1 502 0.89 0.89 0.86 0.82 2.8 1.2
## norm_leg2 501 0.88 0.88 0.84 0.80 2.6 1.2
## norm_leg3 500 0.87 0.87 0.82 0.79 2.2 1.3
## norm_leg4 502 0.88 0.89 0.86 0.82 2.8 1.1
## norm_leg5 502 0.82 0.82 0.75 0.72 2.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## norm_leg1 0.09 0.08 0.15 0.34 0.34 0.00
## norm_leg2 0.09 0.12 0.17 0.36 0.27 0.01
## norm_leg3 0.12 0.19 0.21 0.29 0.19 0.01
## norm_leg4 0.06 0.08 0.18 0.38 0.30 0.00
## norm_leg5 0.07 0.08 0.21 0.35 0.28 0.00
2.8 Non-normative Legitimacy
##
## Reliability analysis
## Call: alpha(x = df[, c("nonnorm_leg1", "nonnorm_leg2", "nonnorm_leg3",
## "nonnorm_leg4", "nonnorm_leg5")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.83 0.53 5.7 0.01 2.8 0.96 0.52
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.85 0.87
## Duhachek 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## nonnorm_leg1 0.84 0.84 0.80 0.57 5.2 0.011 0.0051 0.59
## nonnorm_leg2 0.83 0.84 0.80 0.56 5.1 0.012 0.0057 0.58
## nonnorm_leg3 0.81 0.81 0.78 0.52 4.4 0.013 0.0057 0.52
## nonnorm_leg4 0.81 0.81 0.78 0.52 4.4 0.013 0.0066 0.51
## nonnorm_leg5 0.79 0.79 0.75 0.49 3.8 0.015 0.0036 0.47
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## nonnorm_leg1 503 0.74 0.74 0.63 0.58 2.8 1.2
## nonnorm_leg2 502 0.72 0.75 0.65 0.60 3.1 1.0
## nonnorm_leg3 503 0.82 0.80 0.74 0.68 2.2 1.4
## nonnorm_leg4 503 0.82 0.81 0.74 0.69 2.8 1.3
## nonnorm_leg5 502 0.86 0.86 0.83 0.76 3.0 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## nonnorm_leg1 0.07 0.10 0.17 0.31 0.35 0
## nonnorm_leg2 0.02 0.07 0.12 0.36 0.44 0
## nonnorm_leg3 0.14 0.22 0.14 0.28 0.22 0
## nonnorm_leg4 0.09 0.13 0.09 0.32 0.38 0
## nonnorm_leg5 0.06 0.10 0.08 0.36 0.40 0
2.9 Difference Scores
This is the difference between expected procedural justice and specific procedural justice.
##
## Reliability analysis
## Call: alpha(x = df[, c("diff1", "diff2", "diff3", "diff4", "diff5",
## "diff6")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.93 0.67 12 0.005 -0.79 1.4 0.7
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## diff1 0.90 0.90 0.90 0.64 9.0 0.0069 0.0094 0.64
## diff2 0.90 0.90 0.90 0.65 9.2 0.0068 0.0095 0.66
## diff3 0.91 0.91 0.92 0.68 10.6 0.0055 0.0154 0.70
## diff4 0.91 0.91 0.92 0.68 10.7 0.0058 0.0111 0.70
## diff5 0.91 0.91 0.91 0.67 10.3 0.0060 0.0121 0.69
## diff6 0.93 0.93 0.93 0.71 12.4 0.0051 0.0086 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## diff1 501 0.92 0.91 0.91 0.88 -0.848 1.8
## diff2 498 0.91 0.90 0.90 0.86 -0.908 1.9
## diff3 498 0.82 0.84 0.80 0.75 -0.199 1.4
## diff4 502 0.85 0.84 0.80 0.77 -1.335 1.8
## diff5 499 0.86 0.85 0.82 0.79 -1.429 1.7
## diff6 502 0.74 0.77 0.71 0.67 -0.018 1.2
##
## Non missing response frequency for each item
## -4 -3 -2 -1 0 1 2 3 4 miss
## diff1 0.13 0.10 0.07 0.16 0.34 0.13 0.04 0.02 0.01 0.01
## diff2 0.14 0.11 0.10 0.14 0.30 0.11 0.06 0.03 0.00 0.01
## diff3 0.03 0.03 0.08 0.19 0.44 0.12 0.07 0.02 0.01 0.01
## diff4 0.15 0.16 0.16 0.17 0.23 0.08 0.02 0.03 0.00 0.00
## diff5 0.17 0.12 0.18 0.17 0.22 0.08 0.03 0.01 0.00 0.01
## diff6 0.01 0.02 0.04 0.21 0.49 0.14 0.06 0.03 0.01 0.00
2.10 Brief Self Control
##
## Reliability analysis
## Call: alpha(x = df[, c("BSC1", "BSC2R", "BSC3R", "BSC4R", "BSC5R",
## "BSC6", "BSC7R", "BSC8", "BSC9R", "BSC10R", "BSC11", "BSC12R",
## "BSC13R")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.91 0.4 8.8 0.0068 2.6 0.75 0.41
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.91
## Duhachek 0.88 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BSC1 0.89 0.89 0.89 0.40 7.9 0.0075 0.0091 0.41
## BSC2R 0.88 0.89 0.90 0.39 7.8 0.0076 0.0109 0.40
## BSC3R 0.89 0.89 0.90 0.40 8.0 0.0075 0.0111 0.41
## BSC4R 0.89 0.90 0.90 0.42 8.6 0.0070 0.0085 0.42
## BSC5R 0.89 0.89 0.90 0.40 7.9 0.0075 0.0106 0.41
## BSC6 0.89 0.89 0.90 0.41 8.4 0.0072 0.0096 0.41
## BSC7R 0.89 0.89 0.90 0.40 8.1 0.0073 0.0109 0.41
## BSC8 0.89 0.89 0.90 0.41 8.4 0.0071 0.0079 0.42
## BSC9R 0.89 0.89 0.90 0.40 7.9 0.0075 0.0106 0.41
## BSC10R 0.89 0.89 0.90 0.41 8.3 0.0072 0.0114 0.42
## BSC11 0.89 0.89 0.90 0.41 8.2 0.0073 0.0110 0.41
## BSC12R 0.89 0.89 0.90 0.40 7.9 0.0074 0.0105 0.40
## BSC13R 0.89 0.89 0.90 0.41 8.2 0.0073 0.0106 0.41
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BSC1 503 0.73 0.72 0.71 0.67 2.2 1.13
## BSC2R 501 0.74 0.74 0.71 0.68 2.4 1.16
## BSC3R 503 0.70 0.70 0.67 0.64 3.0 1.06
## BSC4R 500 0.55 0.56 0.51 0.47 2.9 1.06
## BSC5R 501 0.71 0.72 0.70 0.65 2.7 1.04
## BSC6 501 0.62 0.62 0.59 0.54 2.2 1.14
## BSC7R 501 0.68 0.67 0.63 0.60 2.2 1.31
## BSC8 503 0.62 0.60 0.58 0.53 1.7 1.24
## BSC9R 502 0.71 0.72 0.69 0.65 2.7 1.11
## BSC10R 501 0.64 0.64 0.59 0.56 2.8 1.20
## BSC11 502 0.66 0.66 0.62 0.59 2.7 1.07
## BSC12R 502 0.70 0.71 0.69 0.64 3.0 0.99
## BSC13R 503 0.64 0.65 0.62 0.57 3.1 1.00
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## BSC1 0.05 0.25 0.28 0.27 0.15 0.00
## BSC2R 0.07 0.17 0.23 0.36 0.17 0.01
## BSC3R 0.04 0.07 0.15 0.38 0.37 0.00
## BSC4R 0.04 0.07 0.17 0.42 0.30 0.01
## BSC5R 0.04 0.07 0.23 0.42 0.23 0.01
## BSC6 0.06 0.25 0.26 0.30 0.13 0.01
## BSC7R 0.15 0.17 0.22 0.29 0.17 0.01
## BSC8 0.21 0.25 0.28 0.17 0.10 0.00
## BSC9R 0.04 0.11 0.22 0.36 0.27 0.00
## BSC10R 0.06 0.11 0.18 0.31 0.34 0.01
## BSC11 0.03 0.11 0.26 0.34 0.26 0.00
## BSC12R 0.02 0.07 0.16 0.37 0.38 0.00
## BSC13R 0.03 0.06 0.14 0.37 0.41 0.00
3 Correlations
| Distributive Justice | Police Effectiveness | Legal Cynicism | Expected Procedural Justice | Global Procedural Justice | Specific Procedural Justice | Social Desirability Scale | Normative Legitimacy | Non-norm Legitimacy | |
|---|---|---|---|---|---|---|---|---|---|
| Distributive Justice | 1.00 | 0.76* | -0.60* | 0.39* | 0.74* | 0.16* | 0.24* | 0.54* | -0.35* |
| Police Effectiveness | 0.76* | 1.00 | -0.54* | 0.37* | 0.73* | 0.15* | 0.21* | 0.50* | -0.36* |
| Legal Cynicism | -0.60* | -0.54* | 1.00 | -0.26* | -0.63* | -0.21* | -0.30* | -0.43* | 0.38* |
| Expected Procedural Justice | 0.39* | 0.37* | -0.26* | 1.00 | 0.42* | 0.18* | 0.19* | 0.42* | -0.24* |
| Global Procedural Justice | 0.74* | 0.73* | -0.63* | 0.42* | 1.00 | 0.18* | 0.19* | 0.56* | -0.40* |
| Specific Procedural Justice | 0.16* | 0.15* | -0.21* | 0.18* | 0.18* | 1.00 | 0.13* | 0.24* | -0.30* |
| Social Desirability Scale | 0.24* | 0.21* | -0.30* | 0.19* | 0.19* | 0.13* | 1.00 | 0.25* | -0.20* |
| Normative Legitimacy | 0.54* | 0.50* | -0.43* | 0.42* | 0.56* | 0.24* | 0.25* | 1.00 | -0.23* |
| Non-norm Legitimacy | -0.35* | -0.36* | 0.38* | -0.24* | -0.40* | -0.30* | -0.20* | -0.23* | 1.00 |
| Difference Scores | Normative Legitimacy | Non-norm Legitimacy | |
|---|---|---|---|
| Difference Scores | 1.00 | 0.11* | 0.07 |
| Normative Legitimacy | 0.11* | 1.00 | -0.23* |
| Non-norm Legitimacy | 0.07 | -0.23* | 1.00 |
3.1 Scatterplots
4 Qualitative Data
##
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj +
## dist_just + pol_effect + legal_cyn + open_quest + open_quest_count_words,
## data = df_temp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3025 -0.5255 0.0422 0.5207 2.0361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1918188 0.2677850 4.451 1.07e-05 ***
## video_condition1 0.2640244 0.0943499 2.798 0.005349 **
## video_condition2 0.2316724 0.0947635 2.445 0.014865 *
## global_pj 0.2246129 0.0611420 3.674 0.000267 ***
## expected_pj 0.1932743 0.0413919 4.669 3.96e-06 ***
## dist_just 0.1930098 0.0538442 3.585 0.000373 ***
## pol_effect 0.0262491 0.0584421 0.449 0.653534
## legal_cyn -0.0869702 0.0605507 -1.436 0.151582
## open_quest 0.0005127 0.0012565 0.408 0.683408
## open_quest_count_words -0.0033612 0.0070390 -0.478 0.633224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8329 on 466 degrees of freedom
## Multiple R-squared: 0.3894, Adjusted R-squared: 0.3776
## F-statistic: 33.02 on 9 and 466 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj +
## dist_just + pol_effect + legal_cyn + open_quest + open_quest_count_words +
## BSC + SDS + region_split + community_split + race_split +
## age + arrested + pol_contact + pol_fam + citizen + SES +
## Male_split, data = df_temp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3274 -0.4967 0.0616 0.5062 2.1203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4240172 0.4147630 1.022 0.30718
## video_condition1 0.2530985 0.0955393 2.649 0.00835 **
## video_condition2 0.2285719 0.0966679 2.365 0.01847 *
## global_pj 0.2597262 0.0619922 4.190 3.36e-05 ***
## expected_pj 0.1804052 0.0423043 4.264 2.44e-05 ***
## dist_just 0.1781332 0.0557886 3.193 0.00151 **
## pol_effect 0.0285342 0.0588374 0.485 0.62793
## legal_cyn -0.0429114 0.0629648 -0.682 0.49589
## open_quest 0.0006344 0.0012610 0.503 0.61518
## open_quest_count_words -0.0040677 0.0070754 -0.575 0.56563
## BSC 0.0196509 0.0633885 0.310 0.75670
## SDS 0.4214778 0.1830975 2.302 0.02179 *
## region_split 0.0448371 0.0811383 0.553 0.58081
## community_split 0.0129281 0.1022360 0.126 0.89943
## race_split 0.0143609 0.0876965 0.164 0.87000
## age -0.0017978 0.0027798 -0.647 0.51812
## arrested 0.0539909 0.1016583 0.531 0.59561
## pol_contact -0.0492459 0.0850106 -0.579 0.56268
## pol_fam 0.0630640 0.1131925 0.557 0.57771
## citizen 0.3273103 0.2477124 1.321 0.18706
## SES -0.0067059 0.0400689 -0.167 0.86716
## Male_split 0.2050022 0.0778363 2.634 0.00873 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8287 on 454 degrees of freedom
## Multiple R-squared: 0.4112, Adjusted R-squared: 0.3839
## F-statistic: 15.1 on 21 and 454 DF, p-value: < 2.2e-16## Analysis of Variance Table
##
## Model 1: norm_leg ~ video_condition + global_pj + expected_pj + dist_just +
## pol_effect + legal_cyn + open_quest + open_quest_count_words
## Model 2: norm_leg ~ video_condition + global_pj + expected_pj + dist_just +
## pol_effect + legal_cyn + open_quest + open_quest_count_words +
## BSC + SDS + region_split + community_split + race_split +
## age + arrested + pol_contact + pol_fam + citizen + SES +
## Male_split
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 466 323.29
## 2 454 311.77 12 11.527 1.3988 0.1627
5 Regressions
5.1 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, and Controls
##
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj +
## dist_just + pol_effect + legal_cyn + BSC + SDS + region_split +
## community_split + race_split + age + arrested + pol_contact +
## pol_fam + citizen + SES + Male_split, data = df_temp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3231 -0.4859 0.0636 0.4985 2.1292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.392687 0.412129 0.953 0.34118
## video_condition1 0.251563 0.095263 2.641 0.00856 **
## video_condition2 0.230762 0.096122 2.401 0.01676 *
## global_pj 0.260874 0.061875 4.216 3.00e-05 ***
## expected_pj 0.178316 0.042095 4.236 2.75e-05 ***
## dist_just 0.171639 0.055175 3.111 0.00198 **
## pol_effect 0.031607 0.058614 0.539 0.58998
## legal_cyn -0.049864 0.062294 -0.800 0.42386
## BSC 0.018546 0.063282 0.293 0.76960
## SDS 0.426654 0.182699 2.335 0.01996 *
## region_split 0.048864 0.080870 0.604 0.54599
## community_split 0.016364 0.102013 0.160 0.87263
## race_split 0.015553 0.087450 0.178 0.85892
## age -0.001388 0.002733 -0.508 0.61181
## arrested 0.053325 0.101464 0.526 0.59945
## pol_contact -0.044617 0.084663 -0.527 0.59846
## pol_fam 0.065639 0.112789 0.582 0.56088
## citizen 0.326311 0.247363 1.319 0.18778
## SES -0.006859 0.039995 -0.171 0.86391
## Male_split 0.201546 0.077580 2.598 0.00968 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8275 on 456 degrees of freedom
## Multiple R-squared: 0.4102, Adjusted R-squared: 0.3857
## F-statistic: 16.69 on 19 and 456 DF, p-value: < 2.2e-16
5.2 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls
##
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj_cent + expected_pj +
## video_condition * global_pj_cent + dist_just + pol_effect +
## legal_cyn + BSC + SDS + region_split + community_split +
## race_split + age + arrested + pol_contact + pol_fam + citizen +
## SES + Male_split, data = df_temp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3184 -0.5021 0.0524 0.4956 2.1194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.931574 0.405601 2.297 0.02209 *
## video_condition1 0.253016 0.095533 2.648 0.00837 **
## video_condition2 0.228590 0.096302 2.374 0.01803 *
## global_pj_cent 0.305892 0.077831 3.930 9.81e-05 ***
## expected_pj 0.174254 0.042383 4.111 4.67e-05 ***
## dist_just 0.170913 0.055241 3.094 0.00210 **
## pol_effect 0.034371 0.058874 0.584 0.55964
## legal_cyn -0.055936 0.062887 -0.889 0.37422
## BSC 0.016081 0.063427 0.254 0.79997
## SDS 0.430983 0.182966 2.356 0.01892 *
## region_split 0.043293 0.081155 0.533 0.59398
## community_split 0.012769 0.102275 0.125 0.90070
## race_split 0.010297 0.087740 0.117 0.90663
## age -0.001109 0.002756 -0.402 0.68763
## arrested 0.052945 0.101577 0.521 0.60246
## pol_contact -0.036345 0.085164 -0.427 0.66975
## pol_fam 0.058959 0.113545 0.519 0.60383
## citizen 0.329171 0.248036 1.327 0.18514
## SES -0.010025 0.040172 -0.250 0.80306
## Male_split 0.206220 0.077813 2.650 0.00833 **
## video_condition1:global_pj_cent -0.062106 0.088837 -0.699 0.48485
## video_condition2:global_pj_cent -0.084950 0.087981 -0.966 0.33478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8284 on 454 degrees of freedom
## Multiple R-squared: 0.4115, Adjusted R-squared: 0.3843
## F-statistic: 15.12 on 21 and 454 DF, p-value: < 2.2e-16
5.3 Specific PJ is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls
##
## Call:
## lm(formula = specific_pj ~ video_condition + global_pj_cent +
## expected_pj + video_condition * global_pj_cent + dist_just +
## pol_effect + legal_cyn + BSC + SDS + region_split + community_split +
## race_split + age + arrested + pol_contact + pol_fam + citizen +
## SES + Male_split, data = df_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.19102 -0.39413 0.02711 0.37327 2.45579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.235979 0.318739 3.878 0.000121 ***
## video_condition1 1.178087 0.075074 15.692 < 2e-16 ***
## video_condition2 2.277925 0.075678 30.100 < 2e-16 ***
## global_pj_cent 0.107590 0.061163 1.759 0.079239 .
## expected_pj 0.115728 0.033306 3.475 0.000561 ***
## dist_just 0.005667 0.043411 0.131 0.896203
## pol_effect 0.025298 0.046266 0.547 0.584785
## legal_cyn -0.085350 0.049419 -1.727 0.084838 .
## BSC 0.029650 0.049843 0.595 0.552227
## SDS -0.042791 0.143782 -0.298 0.766138
## region_split -0.084298 0.063775 -1.322 0.186901
## community_split -0.035990 0.080372 -0.448 0.654511
## race_split 0.008293 0.068950 0.120 0.904320
## age -0.003286 0.002166 -1.517 0.129860
## arrested 0.111610 0.079823 1.398 0.162731
## pol_contact -0.008184 0.066925 -0.122 0.902727
## pol_fam -0.085232 0.089228 -0.955 0.339979
## citizen -0.132609 0.194917 -0.680 0.496639
## SES 0.021137 0.031569 0.670 0.503478
## Male_split 0.075695 0.061149 1.238 0.216401
## video_condition1:global_pj_cent 0.120503 0.069812 1.726 0.085009 .
## video_condition2:global_pj_cent -0.051740 0.069139 -0.748 0.454635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.651 on 454 degrees of freedom
## Multiple R-squared: 0.7071, Adjusted R-squared: 0.6936
## F-statistic: 52.2 on 21 and 454 DF, p-value: < 2.2e-16
5.4 Non-normative Legitibacy is predicted by Video Condition, Global PJ, Expected PJ, VC*GPJ, and Controls
##
## Call:
## lm(formula = nonnorm_leg ~ video_condition + global_pj_cent +
## expected_pj + video_condition * global_pj_cent + dist_just +
## pol_effect + legal_cyn + SDS + BSC + age + region_split +
## community_split + race_split + arrested + pol_contact + pol_fam +
## citizen + SES + Male_split, data = df_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6859 -0.4894 0.1001 0.5375 2.2146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.764368 0.405266 9.289 < 2e-16 ***
## video_condition1 -0.108064 0.095454 -1.132 0.25819
## video_condition2 -0.495975 0.096223 -5.154 3.8e-07 ***
## global_pj_cent -0.192371 0.077767 -2.474 0.01374 *
## expected_pj -0.031474 0.042348 -0.743 0.45773
## dist_just 0.094993 0.055196 1.721 0.08593 .
## pol_effect -0.126131 0.058826 -2.144 0.03255 *
## legal_cyn 0.189406 0.062835 3.014 0.00272 **
## SDS -0.065537 0.182814 -0.358 0.72014
## BSC -0.220548 0.063374 -3.480 0.00055 ***
## age -0.007669 0.002754 -2.785 0.00558 **
## region_split -0.047249 0.081088 -0.583 0.56039
## community_split 0.084473 0.102190 0.827 0.40888
## race_split 0.160764 0.087668 1.834 0.06734 .
## arrested 0.097598 0.101493 0.962 0.33675
## pol_contact -0.072336 0.085093 -0.850 0.39573
## pol_fam -0.080384 0.113451 -0.709 0.47898
## citizen -0.247774 0.247831 -1.000 0.31795
## SES 0.077008 0.040139 1.919 0.05567 .
## Male_split 0.082355 0.077748 1.059 0.29005
## video_condition1:global_pj_cent 0.106778 0.088764 1.203 0.22962
## video_condition2:global_pj_cent 0.014126 0.087908 0.161 0.87241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8277 on 454 degrees of freedom
## Multiple R-squared: 0.2964, Adjusted R-squared: 0.2639
## F-statistic: 9.108 on 21 and 454 DF, p-value: < 2.2e-16
5.5 Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls
##
## Call:
## lm(formula = norm_leg ~ video_condition + global_pj + expected_pj_cent +
## video_condition * expected_pj_cent + dist_just + pol_effect +
## legal_cyn + SDS + BSC + age + region_split + community_split +
## race_split + arrested + pol_contact + pol_fam + citizen +
## SES + Male_split, data = df_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2949 -0.4949 0.0602 0.5027 2.1495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.977019 0.415826 2.350 0.019222 *
## video_condition1 0.255166 0.095629 2.668 0.007896 **
## video_condition2 0.229073 0.096266 2.380 0.017744 *
## global_pj 0.256553 0.062204 4.124 4.42e-05 ***
## expected_pj_cent 0.219995 0.064411 3.415 0.000694 ***
## dist_just 0.169293 0.055474 3.052 0.002408 **
## pol_effect 0.031740 0.058693 0.541 0.588927
## legal_cyn -0.056521 0.062969 -0.898 0.369880
## SDS 0.432571 0.183062 2.363 0.018549 *
## BSC 0.017935 0.063830 0.281 0.778846
## age -0.001221 0.002743 -0.445 0.656546
## region_split 0.046861 0.081489 0.575 0.565533
## community_split 0.017566 0.102169 0.172 0.863569
## race_split 0.007304 0.088097 0.083 0.933959
## arrested 0.048818 0.101720 0.480 0.631514
## pol_contact -0.042589 0.085212 -0.500 0.617459
## pol_fam 0.067553 0.112969 0.598 0.550150
## citizen 0.326116 0.247685 1.317 0.188619
## SES -0.010288 0.040297 -0.255 0.798599
## Male_split 0.204278 0.077790 2.626 0.008931 **
## video_condition1:expected_pj_cent -0.081897 0.093549 -0.875 0.381791
## video_condition2:expected_pj_cent -0.052657 0.090941 -0.579 0.562862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8286 on 454 degrees of freedom
## Multiple R-squared: 0.4113, Adjusted R-squared: 0.3841
## F-statistic: 15.1 on 21 and 454 DF, p-value: < 2.2e-16
5.6 Specific PJ is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls
##
## Call:
## lm(formula = specific_pj ~ video_condition + global_pj + expected_pj_cent +
## video_condition * expected_pj_cent + dist_just + pol_effect +
## legal_cyn + SDS + BSC + age + region_split + community_split +
## race_split + arrested + pol_contact + pol_fam + citizen +
## SES + Male_split, data = df_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.28252 -0.42840 0.00699 0.36869 2.39533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2661676 0.3257163 3.887 0.000116 ***
## video_condition1 1.1796221 0.0749058 15.748 < 2e-16 ***
## video_condition2 2.2887881 0.0754050 30.353 < 2e-16 ***
## global_pj 0.1414997 0.0487244 2.904 0.003863 **
## expected_pj_cent 0.0007816 0.0504532 0.015 0.987646
## dist_just 0.0113963 0.0434525 0.262 0.793232
## pol_effect 0.0168763 0.0459741 0.367 0.713728
## legal_cyn -0.0780987 0.0493238 -1.583 0.114030
## SDS -0.0633596 0.1433919 -0.442 0.658799
## BSC 0.0346824 0.0499978 0.694 0.488238
## age -0.0034118 0.0021489 -1.588 0.113059
## region_split -0.0791152 0.0638300 -1.239 0.215813
## community_split -0.0305922 0.0800290 -0.382 0.702444
## race_split 0.0255444 0.0690060 0.370 0.711424
## arrested 0.1231090 0.0796773 1.545 0.123020
## pol_contact -0.0142083 0.0667468 -0.213 0.831525
## pol_fam -0.0697315 0.0884884 -0.788 0.431091
## citizen -0.1047815 0.1940116 -0.540 0.589408
## SES 0.0321699 0.0315642 1.019 0.308656
## Male_split 0.0700888 0.0609332 1.150 0.250644
## video_condition1:expected_pj_cent 0.2154642 0.0732766 2.940 0.003445 **
## video_condition2:expected_pj_cent 0.1442486 0.0712339 2.025 0.043452 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.649 on 454 degrees of freedom
## Multiple R-squared: 0.7089, Adjusted R-squared: 0.6954
## F-statistic: 52.65 on 21 and 454 DF, p-value: < 2.2e-16
5.7 Non-normative Legitimacy is predicted by Video Condition, Global PJ, Expected PJ, VC*ExPJ, and Controls
##
## Call:
## lm(formula = nonnorm_leg ~ video_condition + global_pj + expected_pj_cent +
## video_condition * expected_pj_cent + dist_just + pol_effect +
## legal_cyn + SDS + BSC + age + region_split + community_split +
## race_split + arrested + pol_contact + pol_fam + citizen +
## SES + Male_split, data = df_new)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7654 -0.4775 0.1097 0.5612 2.1563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.954077 0.415924 9.507 < 2e-16 ***
## video_condition1 -0.104293 0.095651 -1.090 0.276139
## video_condition2 -0.493710 0.096289 -5.127 4.36e-07 ***
## global_pj -0.157355 0.062219 -2.529 0.011775 *
## expected_pj_cent -0.018064 0.064426 -0.280 0.779311
## dist_just 0.097299 0.055487 1.754 0.080183 .
## pol_effect -0.129984 0.058707 -2.214 0.027316 *
## legal_cyn 0.182314 0.062984 2.895 0.003979 **
## SDS -0.064063 0.183105 -0.350 0.726598
## BSC -0.213886 0.063845 -3.350 0.000875 ***
## age -0.007326 0.002744 -2.670 0.007866 **
## region_split -0.057551 0.081508 -0.706 0.480503
## community_split 0.089306 0.102193 0.874 0.382637
## race_split 0.156348 0.088117 1.774 0.076682 .
## arrested 0.096866 0.101744 0.952 0.341576
## pol_contact -0.061458 0.085232 -0.721 0.471243
## pol_fam -0.072470 0.112996 -0.641 0.521619
## citizen -0.229892 0.247744 -0.928 0.353930
## SES 0.074131 0.040306 1.839 0.066537 .
## Male_split 0.084077 0.077809 1.081 0.280468
## video_condition1:expected_pj_cent 0.004317 0.093571 0.046 0.963224
## video_condition2:expected_pj_cent -0.059649 0.090962 -0.656 0.512311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8288 on 454 degrees of freedom
## Multiple R-squared: 0.2946, Adjusted R-squared: 0.262
## F-statistic: 9.03 on 21 and 454 DF, p-value: < 2.2e-16
6 Path Analyses
6.1 Path Analysis 1 - Normative Legitimacy with Contrls: Male, Region, SDS
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.
##
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## Attaching package: 'kableExtra'
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## The following object is masked from 'package:dplyr':
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## group_rows## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 18
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 3.119
## Degrees of freedom 5
## P-value (Chi-square) 0.682
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## Model Test Baseline Model:
##
## Test statistic 871.105
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.009
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1100.028
## Loglikelihood unrestricted model (H1) -1098.469
##
## Akaike (AIC) 2236.057
## Bayesian (BIC) 2311.956
## Sample-size adjusted Bayesian (SABIC) 2254.822
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.048
## P-value H_0: RMSEA <= 0.050 0.957
## P-value H_0: RMSEA >= 0.080 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.005
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.064 0.113 0.568 0.570 0.064 0.029
## VC2 (c2) -0.071 0.160 -0.441 0.659 -0.071 -0.031
## PE 0.068 0.054 1.251 0.211 0.068 0.072
## DJ 0.137 0.050 2.740 0.006 0.137 0.167
## LC -0.048 0.059 -0.817 0.414 -0.048 -0.039
## ExPJ 0.172 0.040 4.324 0.000 0.172 0.169
## GPJ 0.248 0.059 4.204 0.000 0.248 0.252
## SPJ (b) 0.107 0.057 1.884 0.060 0.107 0.118
## Male_splt 0.171 0.074 2.319 0.020 0.171 0.081
## regn_splt 0.047 0.077 0.607 0.544 0.047 0.021
## SDS 0.405 0.156 2.596 0.009 0.405 0.095
## SPJ ~
## VC1 (a1) 1.197 0.070 17.026 0.000 1.197 0.481
## VC2 (a2) 2.330 0.071 32.701 0.000 2.330 0.922
## LC (a3) -0.088 0.044 -2.008 0.045 -0.088 -0.063
## ExPJ (a4) 0.115 0.030 3.795 0.000 0.115 0.102
## GPJ (a5) 0.137 0.037 3.749 0.000 0.137 0.126
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.592
## .SPJ 0.416 0.026 15.827 0.000 0.416 0.299
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.128 0.068 1.873 0.061 0.128 0.057
## indirct_VC2_NL 0.248 0.132 1.881 0.060 0.248 0.109
## indirect_LC_NL -0.009 0.007 -1.374 0.169 -0.009 -0.008
## indrct_ExPJ_NL 0.012 0.007 1.688 0.091 0.012 0.012
## indirct_GPJ_NL 0.015 0.009 1.684 0.092 0.015 0.015## npar fmin chisq
## 18.000 0.003 3.119
## df pvalue baseline.chisq
## 5.000 0.682 871.105
## baseline.df baseline.pvalue cfi
## 21.000 0.000 1.000
## tli nnfi rfi
## 1.009 1.009 0.985
## nfi pnfi ifi
## 0.996 0.237 1.002
## rni logl unrestricted.logl
## 1.002 -1100.028 -1098.469
## aic bic ntotal
## 2236.057 2311.956 501.000
## bic2 rmsea rmsea.ci.lower
## 2254.822 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.048 0.900 0.957
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.001 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.005
## srmr_bentler srmr_bentler_nomean crmr
## 0.005 0.005 0.005
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.005 0.005 0.005
## cn_05 cn_01 gfi
## 1779.330 2424.412 0.998
## agfi pgfi mfi
## 0.976 0.064 1.002
## ecvi
## 0.078
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| ExPJ | Expected PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| Male_split | Male |
| region_split | Region |
| SDS | SDS |
| From | To | Std_Estimate | P_Value |
|---|---|---|---|
| DJ | NL | 0.1670373 | 0.0061493 |
| ExPJ | NL | 0.1691915 | 0.0000153 |
| GPJ | NL | 0.2523758 | 0.0000262 |
| Male_split | NL | 0.0806865 | 0.0203738 |
| SDS | NL | 0.0954500 | 0.0094184 |
| VC1 | SPJ | 0.4805679 | 0.0000000 |
| VC2 | SPJ | 0.9223020 | 0.0000000 |
| LC | SPJ | -0.0634683 | 0.0446082 |
| ExPJ | SPJ | 0.1022724 | 0.0001477 |
| GPJ | SPJ | 0.1258041 | 0.0001775 |
6.2 Path Analysis 2 - Normative Legitimacy with GPJ Moderation and controls: Male, Region, SDS
## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 4.024
## Degrees of freedom 7
## P-value (Chi-square) 0.777
##
## Model Test Baseline Model:
##
## Test statistic 879.411
## Degrees of freedom 25
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.012
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1096.328
## Loglikelihood unrestricted model (H1) -1094.316
##
## Akaike (AIC) 2232.655
## Bayesian (BIC) 2316.988
## Sample-size adjusted Bayesian (SABIC) 2253.506
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.037
## P-value H_0: RMSEA <= 0.050 0.986
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.064 0.113 0.568 0.570 0.064 0.029
## VC2 (c2) -0.071 0.160 -0.442 0.659 -0.071 -0.031
## PE 0.068 0.054 1.250 0.211 0.068 0.072
## DJ 0.137 0.050 2.740 0.006 0.137 0.167
## LC -0.048 0.059 -0.817 0.414 -0.048 -0.039
## ExPJ 0.172 0.040 4.324 0.000 0.172 0.169
## GPJ 0.248 0.059 4.202 0.000 0.248 0.252
## SPJ (b) 0.107 0.057 1.884 0.060 0.107 0.119
## Male 0.171 0.074 2.319 0.020 0.171 0.081
## Region 0.047 0.077 0.607 0.544 0.047 0.021
## SDS 0.405 0.156 2.596 0.009 0.405 0.095
## SPJ ~
## VC1 (a1) 0.951 0.151 6.310 0.000 0.951 0.382
## VC2 (a2) 2.435 0.148 16.491 0.000 2.435 0.964
## LC (a3) -0.079 0.044 -1.808 0.071 -0.079 -0.057
## ExPJ (a4) 0.118 0.030 3.887 0.000 0.118 0.104
## GPJ (a5) 0.118 0.052 2.295 0.022 0.118 0.108
## VC1_GPJ (i1) 0.119 0.065 1.827 0.068 0.119 0.118
## VC2_GPJ (i2) -0.055 0.066 -0.836 0.403 -0.055 -0.051
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.592
## .SPJ 0.410 0.026 15.827 0.000 0.410 0.294
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.101 0.056 1.806 0.071 0.101 0.045
## indirct_VC2_NL 0.259 0.139 1.872 0.061 0.259 0.114
## indirect_LC_NL -0.008 0.006 -1.304 0.192 -0.008 -0.007
## indrct_ExPJ_NL 0.013 0.007 1.696 0.090 0.013 0.012
## indirct_GPJ_NL 0.013 0.009 1.456 0.145 0.013 0.013
## in_VC1_GPJ_SPJ 0.013 0.010 1.312 0.190 0.013 0.014
## in_VC2_GPJ_SPJ -0.006 0.008 -0.764 0.445 -0.006 -0.006## npar fmin chisq
## 20.000 0.004 4.024
## df pvalue baseline.chisq
## 7.000 0.777 879.411
## baseline.df baseline.pvalue cfi
## 25.000 0.000 1.000
## tli nnfi rfi
## 1.012 1.012 0.984
## nfi pnfi ifi
## 0.995 0.279 1.003
## rni logl unrestricted.logl
## 1.003 -1096.328 -1094.316
## aic bic ntotal
## 2232.655 2316.988 501.000
## bic2 rmsea rmsea.ci.lower
## 2253.506 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.037 0.900 0.986
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.004
## srmr_bentler srmr_bentler_nomean crmr
## 0.004 0.004 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.004 0.004
## cn_05 cn_01 gfi
## 1752.517 2301.383 0.998
## agfi pgfi mfi
## 0.971 0.067 1.003
## ecvi
## 0.088
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male | Male |
| Region | Region |
| VC1_GPJ | Neut Vid * GPJ |
| VC2_GPJ | Pos Vid * GPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.1670477 | 0.0061493 |
| NL | ExPJ | 0.1692021 | 0.0000153 |
| NL | GPJ | 0.2523915 | 0.0000265 |
| NL | Male | 0.0806916 | 0.0203738 |
| NL | SDS | 0.0954560 | 0.0094184 |
| SPJ | VC1 | 0.3816852 | 0.0000000 |
| SPJ | VC2 | 0.9641786 | 0.0000000 |
| SPJ | ExPJ | 0.1044602 | 0.0001013 |
| SPJ | GPJ | 0.1082698 | 0.0217309 |
6.3 Path Analysis 3 - Legitimacy with ExPJ Mod, & Controls: Male, Region, & SDS
## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 3.405
## Degrees of freedom 7
## P-value (Chi-square) 0.845
##
## Model Test Baseline Model:
##
## Test statistic 880.585
## Degrees of freedom 25
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.015
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1095.432
## Loglikelihood unrestricted model (H1) -1093.729
##
## Akaike (AIC) 2230.863
## Bayesian (BIC) 2315.195
## Sample-size adjusted Bayesian (SABIC) 2251.714
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.031
## P-value H_0: RMSEA <= 0.050 0.992
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.064 0.113 0.568 0.570 0.064 0.029
## VC2 (c2) -0.071 0.160 -0.442 0.659 -0.071 -0.031
## PE 0.068 0.054 1.251 0.211 0.068 0.072
## DJ 0.137 0.050 2.740 0.006 0.137 0.167
## LC -0.048 0.059 -0.817 0.414 -0.048 -0.039
## ExPJ 0.172 0.040 4.324 0.000 0.172 0.169
## GPJ 0.248 0.059 4.203 0.000 0.248 0.252
## SPJ (b) 0.107 0.057 1.884 0.060 0.107 0.118
## Male 0.171 0.074 2.319 0.020 0.171 0.081
## Region 0.047 0.077 0.607 0.544 0.047 0.021
## SDS 0.405 0.156 2.596 0.009 0.405 0.095
## SPJ ~
## VC1 (a1) 0.560 0.222 2.523 0.012 0.560 0.225
## VC2 (a2) 1.985 0.217 9.161 0.000 1.985 0.786
## LC (a3) -0.076 0.044 -1.743 0.081 -0.076 -0.055
## ExPJ (a4) 0.016 0.047 0.336 0.737 0.016 0.014
## GPJ (a5) 0.148 0.036 4.048 0.000 0.148 0.135
## VC1_ExPJ (i1) 0.203 0.067 3.026 0.002 0.203 0.279
## VC2_ExPJ (i2) 0.112 0.066 1.697 0.090 0.112 0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.592
## .SPJ 0.408 0.026 15.827 0.000 0.408 0.293
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.060 0.040 1.510 0.131 0.060 0.027
## indirct_VC2_NL 0.211 0.115 1.846 0.065 0.211 0.093
## indirect_LC_NL -0.008 0.006 -1.279 0.201 -0.008 -0.006
## indrct_ExPJ_NL 0.002 0.005 0.331 0.741 0.002 0.002
## indirct_GPJ_NL 0.016 0.009 1.708 0.088 0.016 0.016
## in_VC1_EPJ_SPJ 0.022 0.013 1.600 0.110 0.022 0.033
## in_VC2_EPJ_SPJ 0.012 0.009 1.261 0.207 0.012 0.018## npar fmin chisq
## 20.000 0.003 3.405
## df pvalue baseline.chisq
## 7.000 0.845 880.585
## baseline.df baseline.pvalue cfi
## 25.000 0.000 1.000
## tli nnfi rfi
## 1.015 1.015 0.986
## nfi pnfi ifi
## 0.996 0.279 1.004
## rni logl unrestricted.logl
## 1.004 -1095.432 -1093.729
## aic bic ntotal
## 2230.863 2315.195 501.000
## bic2 rmsea rmsea.ci.lower
## 2251.714 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.031 0.900 0.992
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.004
## srmr_bentler srmr_bentler_nomean crmr
## 0.004 0.004 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.004 0.004
## cn_05 cn_01 gfi
## 2070.541 2719.066 0.998
## agfi pgfi mfi
## 0.976 0.067 1.004
## ecvi
## 0.087
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male | Male |
| Region | Region |
| VC1_ExPJ | Neut Vid * ExPJ |
| VC2_ExPJ | Pos Vid * ExPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.1670424 | 0.0061493 |
| NL | ExPJ | 0.1691967 | 0.0000153 |
| NL | GPJ | 0.2523835 | 0.0000263 |
| NL | Male | 0.0806890 | 0.0203740 |
| NL | SDS | 0.0954529 | 0.0094184 |
| SPJ | VC1 | 0.2248764 | 0.0116257 |
| SPJ | VC2 | 0.7859074 | 0.0000000 |
| SPJ | GPJ | 0.1352125 | 0.0000516 |
| SPJ | VC1_ExPJ | 0.2792519 | 0.0024805 |
6.4 Path Analysis 4 - Legitimacy with ExPJ Mod, & Controls: Male & SDS
## lavaan 0.6.17 ended normally after 2 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 2.279
## Degrees of freedom 6
## P-value (Chi-square) 0.892
##
## Model Test Baseline Model:
##
## Test statistic 879.091
## Degrees of freedom 23
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.017
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1095.615
## Loglikelihood unrestricted model (H1) -1094.476
##
## Akaike (AIC) 2229.231
## Bayesian (BIC) 2309.346
## Sample-size adjusted Bayesian (SABIC) 2249.039
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.026
## P-value H_0: RMSEA <= 0.050 0.994
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.062 0.113 0.546 0.585 0.062 0.028
## VC2 (c2) -0.073 0.160 -0.458 0.647 -0.073 -0.032
## PE 0.066 0.054 1.225 0.221 0.066 0.070
## DJ 0.138 0.050 2.767 0.006 0.138 0.169
## LC -0.050 0.059 -0.840 0.401 -0.050 -0.040
## ExPJ 0.171 0.040 4.318 0.000 0.171 0.169
## GPJ 0.247 0.059 4.189 0.000 0.247 0.252
## SPJ (b) 0.105 0.057 1.855 0.064 0.105 0.117
## Male_splt 0.173 0.074 2.351 0.019 0.173 0.082
## SDS 0.402 0.156 2.578 0.010 0.402 0.095
## SPJ ~
## VC1 (a1) 0.560 0.222 2.523 0.012 0.560 0.225
## VC2 (a2) 1.985 0.217 9.161 0.000 1.985 0.786
## LC (a3) -0.076 0.044 -1.743 0.081 -0.076 -0.055
## ExPJ (a4) 0.016 0.047 0.336 0.737 0.016 0.014
## GPJ (a5) 0.148 0.036 4.048 0.000 0.148 0.135
## VC1_ExPJ (i1) 0.203 0.067 3.026 0.002 0.203 0.279
## VC2_ExPJ (i2) 0.112 0.066 1.697 0.090 0.112 0.150
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.593
## .SPJ 0.408 0.026 15.827 0.000 0.408 0.293
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.059 0.039 1.495 0.135 0.059 0.026
## indirct_VC2_NL 0.208 0.115 1.818 0.069 0.208 0.092
## indirect_LC_NL -0.008 0.006 -1.270 0.204 -0.008 -0.006
## indrct_ExPJ_NL 0.002 0.005 0.331 0.741 0.002 0.002
## indirct_GPJ_NL 0.015 0.009 1.686 0.092 0.015 0.016
## in_VC1_EPJ_SPJ 0.021 0.013 1.581 0.114 0.021 0.033
## in_VC2_EPJ_SPJ 0.012 0.009 1.252 0.211 0.012 0.017## npar fmin chisq
## 19.000 0.002 2.279
## df pvalue baseline.chisq
## 6.000 0.892 879.091
## baseline.df baseline.pvalue cfi
## 23.000 0.000 1.000
## tli nnfi rfi
## 1.017 1.017 0.990
## nfi pnfi ifi
## 0.997 0.260 1.004
## rni logl unrestricted.logl
## 1.004 -1095.615 -1094.476
## aic bic ntotal
## 2229.231 2309.346 501.000
## bic2 rmsea rmsea.ci.lower
## 2249.039 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.026 0.900 0.994
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.003
## srmr_bentler srmr_bentler_nomean crmr
## 0.003 0.003 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.003 0.003
## cn_05 cn_01 gfi
## 2769.543 3697.472 0.999
## agfi pgfi mfi
## 0.983 0.066 1.004
## ecvi
## 0.080
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male_split | Male Split |
| VC1_ExPJ | Neut Vid * ExPJ |
| VC2_ExPJ | Pos Vid * ExPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.1686203 | 0.0056580 |
| NL | ExPJ | 0.1690319 | 0.0000157 |
| NL | GPJ | 0.2515830 | 0.0000280 |
| NL | Male_split | 0.0817349 | 0.0187210 |
| NL | SDS | 0.0947801 | 0.0099297 |
| SPJ | VC1 | 0.2248764 | 0.0116257 |
| SPJ | VC2 | 0.7859074 | 0.0000000 |
| SPJ | GPJ | 0.1352125 | 0.0000516 |
| SPJ | VC1_ExPJ | 0.2792519 | 0.0024805 |
6.4.1 Path Analysis 4.5 - Legitimacy with ExPJ Mod, & Controls: Male & SDS
## lavaan 0.6.17 ended normally after 3 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 1.947
## Degrees of freedom 5
## P-value (Chi-square) 0.856
##
## Model Test Baseline Model:
##
## Test statistic 873.512
## Degrees of freedom 19
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.014
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1098.239
## Loglikelihood unrestricted model (H1) -1097.265
##
## Akaike (AIC) 2228.478
## Bayesian (BIC) 2295.943
## Sample-size adjusted Bayesian (SABIC) 2245.158
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.034
## P-value H_0: RMSEA <= 0.050 0.987
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.069 0.113 0.613 0.540 0.069 0.031
## VC2 (c2) -0.086 0.160 -0.537 0.591 -0.086 -0.038
## DJ 0.176 0.043 4.086 0.000 0.176 0.215
## ExPJ 0.170 0.040 4.285 0.000 0.170 0.168
## GPJ 0.286 0.053 5.413 0.000 0.286 0.291
## SPJ (b) 0.111 0.056 1.973 0.049 0.111 0.124
## Male_splt 0.174 0.074 2.349 0.019 0.174 0.082
## SDS 0.431 0.153 2.810 0.005 0.431 0.102
## SPJ ~
## VC1 (a1) 0.538 0.222 2.420 0.015 0.538 0.216
## VC2 (a2) 1.978 0.217 9.104 0.000 1.978 0.783
## ExPJ (a4) 0.010 0.047 0.219 0.827 0.010 0.009
## GPJ (a5) 0.186 0.029 6.338 0.000 0.186 0.170
## VC1_ExPJ (i1) 0.213 0.067 3.186 0.001 0.213 0.294
## VC2_ExPJ (i2) 0.118 0.066 1.779 0.075 0.118 0.157
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.669 0.042 15.827 0.000 0.669 0.596
## .SPJ 0.411 0.026 15.827 0.000 0.411 0.295
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.060 0.039 1.529 0.126 0.060 0.027
## indirct_VC2_NL 0.220 0.114 1.928 0.054 0.220 0.097
## indrct_ExPJ_NL 0.001 0.005 0.217 0.828 0.001 0.001
## indirct_GPJ_NL 0.021 0.011 1.884 0.060 0.021 0.021
## in_VC1_EPJ_SPJ 0.024 0.014 1.677 0.093 0.024 0.036
## in_VC2_EPJ_SPJ 0.013 0.010 1.321 0.186 0.013 0.019## npar fmin chisq
## 16.000 0.002 1.947
## df pvalue baseline.chisq
## 5.000 0.856 873.512
## baseline.df baseline.pvalue cfi
## 19.000 0.000 1.000
## tli nnfi rfi
## 1.014 1.014 0.992
## nfi pnfi ifi
## 0.998 0.263 1.004
## rni logl unrestricted.logl
## 1.004 -1098.239 -1097.265
## aic bic ntotal
## 2228.478 2295.943 501.000
## bic2 rmsea rmsea.ci.lower
## 2245.158 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.034 0.900 0.987
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.004
## srmr_bentler srmr_bentler_nomean crmr
## 0.004 0.004 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.004 0.004
## cn_05 cn_01 gfi
## 2849.336 3882.557 0.999
## agfi pgfi mfi
## 0.988 0.076 1.003
## ecvi
## 0.068
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| DJ | Distributive Justice |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male_split | Male Split |
| VC1_ExPJ | Neut Vid * ExPJ |
| VC2_ExPJ | Pos Vid * ExPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.2145094 | 0.0000439 |
| NL | ExPJ | 0.1676499 | 0.0000183 |
| NL | GPJ | 0.2913645 | 0.0000001 |
| NL | SPJ | 0.1239186 | 0.0485139 |
| NL | Male_split | 0.0818678 | 0.0188158 |
| NL | SDS | 0.1016395 | 0.0049486 |
| SPJ | VC1 | 0.2160138 | 0.0154994 |
| SPJ | VC2 | 0.7831961 | 0.0000000 |
| SPJ | GPJ | 0.1700561 | 0.0000000 |
| SPJ | VC1_ExPJ | 0.2937562 | 0.0014413 |
6.4.2 Path Analysis 4.5 - Cross-validation
## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 251
##
## Model Test User Model:
##
## Test statistic 1.531
## Degrees of freedom 5
## P-value (Chi-square) 0.909
##
## Model Test Baseline Model:
##
## Test statistic 437.658
## Degrees of freedom 19
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.031
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -543.882
## Loglikelihood unrestricted model (H1) -543.117
##
## Akaike (AIC) 1119.765
## Bayesian (BIC) 1176.172
## Sample-size adjusted Bayesian (SABIC) 1125.450
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.034
## P-value H_0: RMSEA <= 0.050 0.974
## P-value H_0: RMSEA >= 0.080 0.004
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.152 0.157 0.969 0.333 0.152 0.071
## VC2 (c2) -0.065 0.225 -0.290 0.772 -0.065 -0.030
## DJ 0.214 0.060 3.553 0.000 0.214 0.265
## ExPJ 0.175 0.056 3.148 0.002 0.175 0.178
## GPJ 0.215 0.075 2.880 0.004 0.215 0.226
## SPJ (b) 0.114 0.077 1.476 0.140 0.114 0.130
## Male_splt 0.110 0.102 1.074 0.283 0.110 0.053
## SDS 0.434 0.206 2.108 0.035 0.434 0.106
## SPJ ~
## VC1 (a1) 0.469 0.316 1.485 0.138 0.469 0.192
## VC2 (a2) 2.018 0.301 6.703 0.000 2.018 0.806
## ExPJ (a4) 0.009 0.072 0.121 0.904 0.009 0.008
## GPJ (a5) 0.193 0.042 4.567 0.000 0.193 0.178
## VC1_ExPJ (i1) 0.243 0.097 2.496 0.013 0.243 0.339
## VC2_ExPJ (i2) 0.128 0.094 1.358 0.174 0.128 0.169
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.634 0.057 11.203 0.000 0.634 0.592
## .SPJ 0.412 0.037 11.203 0.000 0.412 0.297
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.054 0.051 1.047 0.295 0.054 0.025
## indirct_VC2_NL 0.230 0.160 1.442 0.149 0.230 0.105
## indrct_ExPJ_NL 0.001 0.008 0.120 0.904 0.001 0.001
## indirct_GPJ_NL 0.022 0.016 1.405 0.160 0.022 0.023
## in_VC1_EPJ_SPJ 0.028 0.022 1.271 0.204 0.028 0.044
## in_VC2_EPJ_SPJ 0.015 0.015 1.000 0.318 0.015 0.022## [,1]6.4.3 Path Analysis 4.5 - Bootstrap Validation
## lavaan 0.6.17 ended normally after 3 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 1.947
## Degrees of freedom 5
## P-value (Chi-square) 0.856
##
## Model Test Baseline Model:
##
## Test statistic 873.512
## Degrees of freedom 19
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.014
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1098.239
## Loglikelihood unrestricted model (H1) -1097.265
##
## Akaike (AIC) 2228.478
## Bayesian (BIC) 2295.943
## Sample-size adjusted Bayesian (SABIC) 2245.158
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.034
## P-value H_0: RMSEA <= 0.050 0.987
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.069 0.111 0.622 0.534 0.069 0.031
## VC2 (c2) -0.086 0.161 -0.535 0.593 -0.086 -0.038
## DJ 0.176 0.047 3.747 0.000 0.176 0.215
## ExPJ 0.170 0.048 3.548 0.000 0.170 0.168
## GPJ 0.286 0.061 4.664 0.000 0.286 0.291
## SPJ (b) 0.111 0.059 1.872 0.061 0.111 0.124
## Male_splt 0.174 0.074 2.346 0.019 0.174 0.082
## SDS 0.431 0.168 2.563 0.010 0.431 0.102
## SPJ ~
## VC1 (a1) 0.538 0.212 2.541 0.011 0.538 0.216
## VC2 (a2) 1.978 0.165 11.974 0.000 1.978 0.783
## ExPJ (a4) 0.010 0.039 0.262 0.794 0.010 0.009
## GPJ (a5) 0.186 0.032 5.744 0.000 0.186 0.170
## VC1_ExPJ (i1) 0.213 0.066 3.227 0.001 0.213 0.294
## VC2_ExPJ (i2) 0.118 0.052 2.256 0.024 0.118 0.157
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.669 0.048 13.863 0.000 0.669 0.596
## .SPJ 0.411 0.034 12.078 0.000 0.411 0.295
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.060 0.041 1.464 0.143 0.060 0.027
## indirct_VC2_NL 0.220 0.118 1.860 0.063 0.220 0.097
## indrct_ExPJ_NL 0.001 0.005 0.226 0.821 0.001 0.001
## indirct_GPJ_NL 0.021 0.012 1.769 0.077 0.021 0.021
## in_VC1_EPJ_SPJ 0.024 0.016 1.522 0.128 0.024 0.036
## in_VC2_EPJ_SPJ 0.013 0.010 1.359 0.174 0.013 0.0196.5 Path Analysis 5 - Legitimacy with ExPJ & GPJ Mod, & Controls: Male & SDS
## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 3.554
## Degrees of freedom 8
## P-value (Chi-square) 0.895
##
## Model Test Baseline Model:
##
## Test statistic 886.685
## Degrees of freedom 27
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.017
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1092.456
## Loglikelihood unrestricted model (H1) -1090.679
##
## Akaike (AIC) 2226.912
## Bayesian (BIC) 2315.461
## Sample-size adjusted Bayesian (SABIC) 2248.805
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.023
## P-value H_0: RMSEA <= 0.050 0.997
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.062 0.113 0.546 0.585 0.062 0.028
## VC2 (c2) -0.073 0.160 -0.458 0.647 -0.073 -0.032
## PE 0.066 0.054 1.225 0.221 0.066 0.070
## DJ 0.138 0.050 2.767 0.006 0.138 0.169
## LC -0.050 0.059 -0.840 0.401 -0.050 -0.040
## ExPJ 0.171 0.040 4.318 0.000 0.171 0.169
## GPJ 0.247 0.059 4.188 0.000 0.247 0.252
## SPJ (b) 0.105 0.057 1.855 0.064 0.105 0.117
## Male_splt 0.173 0.074 2.351 0.019 0.173 0.082
## SDS 0.402 0.156 2.578 0.010 0.402 0.095
## SPJ ~
## VC1 (a1) 0.522 0.226 2.314 0.021 0.522 0.210
## VC2 (a2) 2.074 0.220 9.440 0.000 2.074 0.821
## LC (a3) -0.075 0.044 -1.708 0.088 -0.075 -0.054
## ExPJ (a4) -0.001 0.052 -0.026 0.979 -0.001 -0.001
## GPJ (a5) 0.183 0.056 3.267 0.001 0.183 0.167
## VC1_ExPJ (i1) 0.193 0.074 2.614 0.009 0.193 0.266
## VC2_ExPJ (i2) 0.168 0.073 2.299 0.022 0.168 0.225
## VC1_GPJ (i3) 0.033 0.072 0.455 0.649 0.033 0.032
## VC2_GPJ (i4) -0.134 0.073 -1.849 0.064 -0.134 -0.125
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.593
## .SPJ 0.403 0.025 15.827 0.000 0.403 0.290
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.055 0.038 1.447 0.148 0.055 0.024
## indirct_VC2_NL 0.217 0.120 1.820 0.069 0.217 0.096
## indirect_LC_NL -0.008 0.006 -1.256 0.209 -0.008 -0.006
## indrct_ExPJ_NL -0.000 0.005 -0.026 0.979 -0.000 -0.000
## indirct_GPJ_NL 0.019 0.012 1.613 0.107 0.019 0.020
## in_VC1_EPJ_SPJ 0.020 0.013 1.513 0.130 0.020 0.031
## in_VC2_EPJ_SPJ 0.018 0.012 1.443 0.149 0.018 0.026
## in_VC1_GPJ_SPJ 0.003 0.008 0.442 0.659 0.003 0.004
## in_VC2_GPJ_SPJ -0.014 0.011 -1.310 0.190 -0.014 -0.015## npar fmin chisq
## 21.000 0.004 3.554
## df pvalue baseline.chisq
## 8.000 0.895 886.685
## baseline.df baseline.pvalue cfi
## 27.000 0.000 1.000
## tli nnfi rfi
## 1.017 1.017 0.986
## nfi pnfi ifi
## 0.996 0.295 1.005
## rni logl unrestricted.logl
## 1.005 -1092.456 -1090.679
## aic bic ntotal
## 2226.912 2315.461 501.000
## bic2 rmsea rmsea.ci.lower
## 2248.805 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.023 0.900 0.997
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.003
## srmr_bentler srmr_bentler_nomean crmr
## 0.003 0.003 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.003 0.003
## cn_05 cn_01 gfi
## 2186.864 2832.859 0.998
## agfi pgfi mfi
## 0.975 0.067 1.004
## ecvi
## 0.091
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male_split | Male Split |
| VC1_ExPJ | Neut Vid * ExPJ |
| VC2_ExPJ | Pos Vid * ExPJ |
| VC1_GPJ | Neut Vid * GPJ |
| VC2_GPJ | Pos Vid * GPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.1686272 | 0.0056580 |
| NL | ExPJ | 0.1690388 | 0.0000157 |
| NL | GPJ | 0.2515933 | 0.0000282 |
| NL | Male_split | 0.0817382 | 0.0187215 |
| NL | SDS | 0.0947840 | 0.0099300 |
| SPJ | VC1 | 0.2095191 | 0.0206482 |
| SPJ | VC2 | 0.8209326 | 0.0000000 |
| SPJ | GPJ | 0.1671579 | 0.0010875 |
| SPJ | VC1_ExPJ | 0.2663442 | 0.0089575 |
| SPJ | VC2_ExPJ | 0.2246857 | 0.0215273 |
6.6 Path Analysis 6 - Legitimacy & Non-normative Legitimacy with ExPJ Mod, & Controls: Male & SDS
## lavaan 0.6.17 ended normally after 5 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 33
##
## Number of observations 501
##
## Model Test User Model:
##
## Test statistic 4.905
## Degrees of freedom 9
## P-value (Chi-square) 0.843
##
## Model Test Baseline Model:
##
## Test statistic 1031.291
## Degrees of freedom 39
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.018
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1714.226
## Loglikelihood unrestricted model (H1) -1711.773
##
## Akaike (AIC) 3494.452
## Bayesian (BIC) 3633.600
## Sample-size adjusted Bayesian (SABIC) 3528.855
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.029
## P-value H_0: RMSEA <= 0.050 0.996
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## NL ~
## VC1 (c1) 0.064 0.113 0.568 0.570 0.064 0.029
## VC2 (c2) -0.071 0.160 -0.442 0.659 -0.071 -0.031
## PE 0.068 0.054 1.251 0.211 0.068 0.072
## DJ 0.137 0.050 2.740 0.006 0.137 0.167
## LC -0.048 0.059 -0.817 0.414 -0.048 -0.039
## ExPJ 0.172 0.040 4.324 0.000 0.172 0.169
## GPJ 0.248 0.059 4.203 0.000 0.248 0.252
## SPJ (b) 0.107 0.057 1.884 0.060 0.107 0.118
## Male 0.171 0.074 2.319 0.020 0.171 0.081
## Reg 0.047 0.077 0.607 0.544 0.047 0.021
## SDS 0.405 0.156 2.596 0.009 0.405 0.095
## NNL ~
## VC1 (d1) -0.014 0.116 -0.124 0.902 -0.014 -0.007
## VC2 (d2) -0.296 0.164 -1.805 0.071 -0.296 -0.143
## PE -0.102 0.055 -1.838 0.066 -0.102 -0.119
## DJ 0.020 0.051 0.388 0.698 0.020 0.027
## LC 0.176 0.061 2.897 0.004 0.176 0.154
## ExPJ -0.051 0.041 -1.263 0.206 -0.051 -0.056
## GPJ -0.164 0.060 -2.717 0.007 -0.164 -0.183
## SPJ (e) -0.086 0.058 -1.496 0.135 -0.086 -0.106
## Male 0.083 0.076 1.094 0.274 0.083 0.043
## Reg -0.041 0.079 -0.518 0.604 -0.041 -0.020
## SDS -0.260 0.159 -1.629 0.103 -0.260 -0.067
## SPJ ~
## VC1 (a1) 0.560 0.222 2.523 0.012 0.560 0.225
## VC2 (a2) 1.985 0.217 9.161 0.000 1.985 0.786
## LC (a3) -0.076 0.044 -1.743 0.081 -0.076 -0.055
## ExPJ (a4) 0.016 0.047 0.336 0.737 0.016 0.014
## GPJ (a5) 0.148 0.036 4.048 0.000 0.148 0.135
## VC1_ExPJ (i1) 0.203 0.067 3.026 0.002 0.203 0.279
## VC2_ExPJ (i2) 0.112 0.066 1.697 0.090 0.112 0.150
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL ~~
## .NNL 0.053 0.031 1.730 0.084 0.053 0.078
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NL 0.666 0.042 15.827 0.000 0.666 0.592
## .NNL 0.697 0.044 15.827 0.000 0.697 0.747
## .SPJ 0.408 0.026 15.827 0.000 0.408 0.293
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## indirct_VC1_NL 0.060 0.040 1.510 0.131 0.060 0.027
## indirct_VC2_NL 0.211 0.115 1.846 0.065 0.211 0.093
## indirect_LC_NL -0.008 0.006 -1.279 0.201 -0.008 -0.006
## indrct_ExPJ_NL 0.002 0.005 0.331 0.741 0.002 0.002
## indirct_GPJ_NL 0.016 0.009 1.708 0.088 0.016 0.016
## indrct_VC1_NNL -0.048 0.038 -1.287 0.198 -0.048 -0.024
## indrct_VC2_NNL -0.172 0.116 -1.476 0.140 -0.172 -0.083
## indirct_LC_NNL 0.007 0.006 1.135 0.256 0.007 0.006
## indrct_EPJ_NNL -0.001 0.004 -0.328 0.743 -0.001 -0.001
## indrct_GPJ_NNL -0.013 0.009 -1.403 0.161 -0.013 -0.014
## in_VC1_EPJ_SPJ 0.022 0.013 1.600 0.110 0.022 0.033
## in_VC2_EPJ_SPJ 0.012 0.009 1.261 0.207 0.012 0.018## npar fmin chisq
## 33.000 0.005 4.905
## df pvalue baseline.chisq
## 9.000 0.843 1031.291
## baseline.df baseline.pvalue cfi
## 39.000 0.000 1.000
## tli nnfi rfi
## 1.018 1.018 0.979
## nfi pnfi ifi
## 0.995 0.230 1.004
## rni logl unrestricted.logl
## 1.004 -1714.226 -1711.773
## aic bic ntotal
## 3494.452 3633.600 501.000
## bic2 rmsea rmsea.ci.lower
## 3528.855 0.000 0.000
## rmsea.ci.upper rmsea.ci.level rmsea.pvalue
## 0.029 0.900 0.996
## rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
## 0.050 0.000 0.080
## rmr rmr_nomean srmr
## 0.003 0.003 0.004
## srmr_bentler srmr_bentler_nomean crmr
## 0.004 0.004 0.004
## crmr_nomean srmr_mplus srmr_mplus_nomean
## 0.004 0.004 0.004
## cn_05 cn_01 gfi
## 1729.187 2214.071 0.998
## agfi pgfi mfi
## 0.974 0.075 1.004
## ecvi
## 0.142## Some edges involve nodes not in layout. These were dropped.
| Abbreviation | Full_Name |
|---|---|
| VC1 | Neutral Video |
| VC2 | Positive Video |
| PE | Police Effectiveness |
| DJ | Distributive Justice |
| LC | Legal Cynicism |
| GPJ | Global PJ |
| SPJ | Specific PJ |
| NL | Normative Legitimacy |
| NNL | Non-normative Legitimacy |
| ExPJ | Expected PJ |
| SDS | SDS |
| Male | Male |
| VC1_ExPJ | Neut Vid * ExPJ |
| VC2_ExPJ | Pos Vid * ExPJ |
| To | From | Std_Estimate | P_Value |
|---|---|---|---|
| NL | DJ | 0.1670424 | 0.0061493 |
| NL | ExPJ | 0.1691967 | 0.0000153 |
| NL | GPJ | 0.2523835 | 0.0000263 |
| NL | Male | 0.0806890 | 0.0203740 |
| NL | SDS | 0.0954529 | 0.0094184 |
| NNL | LC | 0.1539905 | 0.0037635 |
| NNL | GPJ | -0.1832111 | 0.0065970 |
| SPJ | VC1 | 0.2248764 | 0.0116257 |
| SPJ | VC2 | 0.7859074 | 0.0000000 |
| SPJ | GPJ | 0.1352125 | 0.0000516 |
| SPJ | VC1_ExPJ | 0.2792519 | 0.0024805 |
7 Ranked PJ
## `summarise()` has grouped output by 'rank_position'. You can override using the
## `.groups` argument.
8 Memory
Video condition 0 = Negative video
Video condition 1 = Neutral video
Video condition 2 = Positive video
## `summarise()` has grouped output by 'video_condition'. You can override using
## the `.groups` argument.
9 Text Analysis
9.1 Analysis for Condition 0
## document negative positive sentiment
## 1 1 1 0 -1
## 2 2 1 0 -1
## 3 3 0 1 1
## 4 4 1 0 -1
## 5 5 1 0 -1
## 6 6 1 0 -1## document negative positive sentiment
## 1 1 1 0 -1
## 2 2 1 0 -1
## 3 3 0 1 1
## 4 4 0 1 1
## 5 5 0 1 1
## 6 6 0 1 1## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.0000 -1.0000 -1.0000 -0.4477 1.0000 1.0000## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.0000 -1.0000 -1.0000 -0.3911 1.0000 1.0000
## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`
9.2 Analysis for Condition 1
## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`
9.3 Analysis for Condition 2
## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`
10 Variable List
| Variable | Description |
|---|---|
| Duration (in seconds)) |
| The police provide the same level of security to all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police provide the same quality of service to all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police enforce the law consistently when dealing with all community members. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police deploy their resources in this city in an equitable manner. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police ensure that everyone has equal access to the services they provide. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police do a good job working together with neighborhood residents to reduce crime (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police do a good job dealing with neighborhood problems (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police do a good job ExPJventing crime (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Laws protect everyone equally (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| People with money and power can get away with anything (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Politicians only care about getting re-elected (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Anyone can get ahead if they try hard enough (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Powerful people use laws to disadvantage individuals who do not have any power (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to treat drivers with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to be polite when dealing with drivers (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to be fair when making decisions with drivers (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to give drivers the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to listen to drivers during stops (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I expect the police to make decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police treat people with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police treat people with politeness (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police are fair when making decisions (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police give people the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police take time to listen to people when they stop them (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police make decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer treated the driver with dignity and respect (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer was polite when dealing with the driver (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer was fair when making the decision to the driver (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer gave the driver the opportunity to exExPJss their views (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer listened to the driver during the stop (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The police officer made decisions based upon facts (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 1st response |
| Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 2nd response |
| Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 3rd response |
| Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 4th response |
| Please identify and describe up to five specific behaviors demonstrated by the officer in the video. - 5th response |
| Overall, do you consider the outcome in the video to be just or unjust? Please explain why. |
| I sometimes litter. (False = 0, True = 1) |
| I always admit my mistakes openly and face the potential negative consequences. (False = 0, True = 1) |
| In traffic I am always polite and considerate of others. (False = 0, True = 1) |
| I have tried illegal drugs (for example, marijuana, cocaine, etc.). (False = 0, True = 1) |
| I always accept others’ opinions, even when they don’t agree with my own. (False = 0, True = 1) |
| I take out my bad moods on others now and then. (False = 0, True = 1) |
| There has been an occasion when I took advantage of someone else. (False = 0, True = 1) |
| In conversations I always listen attentively and let others finish their sentences. (False = 0, True = 1) |
| I never hesitate to help someone in case of emergency. (False = 0, True = 1) |
| When I have made a promise, I keep it–no ifs, ands or buts. (False = 0, True = 1) |
| I occasionally speak badly of others behind their back. (False = 0, True = 1) |
| I would never live off other people. (False = 0, True = 1) |
| I always stay friendly and courteous with other people, even when I am stressed out. (False = 0, True = 1) |
| During arguments I always stay objective and matter-of-fact. (False = 0, True = 1) |
| There has been at least one occasion when I failed to return an item that I borrowed. (False = 0, True = 1) |
| I always eat a healthy diet. (False = 0, True = 1) |
| Sometimes I only help because I expect something in return. (False = 0, True = 1) |
| I would feel a moral obligation to obey the police. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I would feel a moral duty to obey the instructions of the police officer even if I don’t understand the reasons behind them. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I would feel a moral duty to support the decisions of the police officer, even if I disagree with them. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I would do what the police officer told me to do because I believe it is the right thing to do. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I believe that the proper thing to do is to accept the decisions that the police officer makes. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| People like me have no choice but to obey the police officer. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| If I didn’t do what the police officer told me, he would treat me badly. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I would only obey the police officer because I am afraid of him. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| The main reason I would obey the police officer is because I am scared of getting in trouble. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I would do what the police officer tells me because I fear how he would react if I didn’t. (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| I am good at resisting temptation. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I have a hard time breaking bad habits. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I am lazy. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I say inappropriate things. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I do certain things that are bad for me, if they are fun. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I refuse things that are bad for me. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I wish I had more self-discipline. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| People would say that I have iron self-discipline. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| Pleasure and fun sometimes keep me from getting work done. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I have trouble concentrating. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I am able to work effectively toward long-term goals. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| Sometimes I can’t stop myself from doing something, even if I know it is wrong. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| I often act without thinking through all the alternatives. (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| How would characterize the police officer’s behavior in the video. |
| Think back to the behavior you expected from the police. How much did the officer act as expected? |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer opens with a polite greeting |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains why they are interacting with the citizen |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer calls the citizen by an appropriate title/name |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer thanks the citizen for your cooperation |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains how to proceed in the legal process |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains the consequences of non-compliance |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer says goodbye to the citizen in a polite manner |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer asked the citizen to provide information/viewpoint |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer indicated he would not make a decision about what to do until s/he had gathered all the necessary information |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explains the policy regarding their actions |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer explained why s/he chose to resolve the situation as s/he did |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer offered comfort or reassurance to this citizen |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer provided or promised to provide advice handling the situation/problem |
| With regards to how the police treat people, please rank the following items in terms of importance, from ‘most important’ to ‘least important’ - The officer indicates how citizen can file a complaint |
| Please specify which behaviors listed below you recall the officer exhibiting during the video you watched. |
| Overall, how fair or unfair was the interaction? |
| What is your Gender? (Male = 1, Female = 0, Other = 0) |
| If you answered ‘other’ to Gender, please specify. |
| Gender was dichotomized (Male = 1, All else = 0) |
| Are you of Hispanic, Latino, or Spanish origin? (No = 0, Yes, Mexican, Mexican American, Chicano = 1, Yes, Puerto Rican = 2, Yes, Cuban = 3, Yes, other Hispanic, Latino, or Spanish origin = 4) |
| What is your race? (select all that apply) (White = 0, Black or African American = 1, American Indian or Alaska Native = 2, Asian = 3, Middle Eastern = 4, Pacific Islander = 5, Other = 6) |
| If you answered ‘other’ to race, please specify |
| Race was dichotomized (White = 0, All else = 1) |
| What is the yearly household income level? (Less than $34,999 = 0, $35k-$49,999 = 1, $50,000-$74,999 = 2, $75k,000-$99,999 = 3, $100k or more = 4) |
| What is the highest level of education you reached? (Less than high school = 0, High school or equivalent diploma, some college, or associate’s degree = 1, Bachelor’s degree = 2, Master’s, professional, or doctoral degree = 3) |
| How would you classify your current occupation in the scale below? (Unemployed = 0, Unskilled manual labor = 1, Skilled manual labor = 2, Professional labor = 3) |
| What is your marital status? (Never married = 0, Not married, but in long term relationship = 1, Married = 2, Divorced = 3, Widowed = 4) |
| Identify the area of the country where you currently live. (Northeast = 0, Midwest = 1, West = 2, South = 3) |
| Region was dichotomized (South = 1, Northeast, Midwest, & West = 0) |
| Select the option that best describes the community where you live. (Urban = 0, Suburban = 1, Rural = 2) |
| Community type was dichotomized (Rural = 1, Urban & Suburban = 0) |
| Generally speaking, do you consider yourself a part of one of the political parties listed below? (Democrat = 0, Republican = 1, Independent = 2, Socialist = 3, Libertarian = 4, Something else = 5, I do not identify with any political party = 6) |
| Where would you place yourself on the following scale. (Very conservative = 0, Conservative = 1, Slightly conservative = 2, Centrist = 3, Slightly Liberal = 4, Liberal = 5, Very Liberal = 6) |
| Are you a homeowner or renter? (Renter = 0, Homeowner = 1) |
| How long have you lived in your current home? |
| Are you a citizen of the United States? (No = 0, Yes = 1) |
| Are you fluent in English? |
| Is there anyone close to you who is a police officer (i.e., family, friends, intimate partner)? (No = 0, Yes = 1) |
| Have you had any personal contact with the police in the past 12 months? |
| Please estimate how many encounters have you had with the police in your lifetime? |
| Please think about that contact or if there was more than one contact, the most recent one. Which of the following best describes your contact with the police? (I called the police to report a crime = 0, I called the police to report an accident = 1, I called the police to request information = 2, I was pulled over by the police while I was driving = 3, Something else = 4) |
| Have you ever been arrested before? (No = 0, Yes = 1) |
| If ‘Yes’, how long ago was your (last) arrest? |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Assault (an unlawful attack by one person upon another for the purpose of inflicting injury). |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Burglary (the unlawful entry of a structure to commit a theft). |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Theft (the unlawful taking of property from the possession of another). |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Vandalism (the destruction or defacement of property without the consent of the owner). |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Internet crime (such as consumer fraud, identity theft, or virus) |
| Using the scale provided below, please tell us whether you have been a victim of any of the following crimes in the past year. - Other (Please specify) |
| If ‘Other’, please describe your victimization experience. |
| How honest were you in answering the questions? (Not at all honest = 0, A little honest = 1, Moderately honest = 2, Very honest = 3, Completely honest = 4) |
| When going through the survey, how carefully did you read the questions? (Not carefully at all = 0, Not very carefully = 1, Moderately careful = 2, Carefully = 3, Extremely carefully = 4) |
| Did the encounter you watched seem realistic? (Definitely not = 0, Probably not = 1, Might or might not = 2, Probably yes = 3, Definitely yes = 4) |
| PROLIFIC_PID |
| Participant age |
| A combined factor for socio-economic status consisting of occupation, education, and income |
| Video conditions: 0 - Negative condition, 1 - Neutral condition, 2 - Positive condition |
| Distributive justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Police effectiveness factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Legal cynicism factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Expected PJ factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Global procedural justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Specific procedural justice factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Social Desirability scale factor (False = 0, True = 1) |
| Normative legitimacy factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Non-normative legitimacy factor (Strongly disagree = 0, Somewhat disagree = 1, Neither agree nor disagree = 2, Somewhat agree = 3, Strongly agree = 4) |
| Brief self control factor (Not at all = 0, A little = 1, Moderately = 2, Quite a bit = 3, Very much = 4) |
| specific_pj1 - expected_pj1 |
| specific_pj1 - expected_pj2 |
| specific_pj1 - expected_pj3 |
| specific_pj1 - expected_pj4 |
| specific_pj1 - expected_pj5 |
| specific_pj1 - expected_pj6 |
| Difference between Specific_PJ - expected_pj factor |
| Global procedural justice mean centered |
| Expected procedural justice mean centered |
| Video conditions abbreviated |
| Expected procedural justice mean centered and abbreviated |
| Global procedural justice mean centered and abbreviated |
| Neutral condition dummy coded |
| Positive condition dummy coded |
| Video condition 1 abbreviated |
| Video condition 2 abbreviated |
| Police effectiveness abbreviated |
| Distributive justice abbreviated |
| Legal cynicism abbreviated |
| Global procedural justice abbreviated |
| Specific procedural justice abbreviated |
| Normative legitimacy abbreviated |
| Non-normative legitimacy abbreviated |
| Expected procedural justice abbreviated |
| Interaction term of Neutral condition x Global PJ |
| Interaction term of Positive condition x Global PJ |
| Have you been arrested before abbreviated |
| Interaction term of Neutral condition x Expected PJ |
| Interaction term of Positive condition x Expected PJ |
2.11 Social Desirability